predictive

predictive delivery

In a webless world where cookies, UTM links, and blue-link clicks have disappeared, Mosaic has built a new way to understand user behavior. Our proprietary knowledge graph correlates signals across prompts, surfaces, and models, creating a living context layer around each interaction. Instead of relying on fragmented attribution, Mosaic maps intent at the moment it emerges—making sense of conversations and actions that legacy ad rails can no longer track.

At the center of this system is Lilypad™, our cross-model tracing technology. Lilypad allows us to follow the flow of prompts across LLMs, agents, and AI-native apps, while respecting anonymity and privacy. Combined with user feedback loops, this gives Mosaic the ability to place advice where it feels most natural and relevant—not disruptive. Our system doesn’t force ads into experiences; it predicts when and how advice should appear to maximize value for the consumer and return for the brand.

The result is a predictive advice engine that consistently drives higher engagement rates than traditional advertising. By aligning intent, timing, and modality (text, image, voice, or video), Mosaic transforms ads into trusted recommendations. Early pilots with major brands already show interaction rates near 3%, validating that predictive, feedback-driven advice outperforms standard digital placements and sets the foundation for monetization across infinity surfaces.